Eva Nittinger
Overview
Explore the profile of Eva Nittinger including associated specialties, affiliations and a list of published articles.
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Articles
23
Citations
339
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0
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Recent Articles
1.
Zhao H, Kwapien K, Nittinger E, Tyrchan C, Nilsson M, Berglund S, et al.
J Chem Inf Model
. 2025 Feb;
65(5):2251-2255.
PMID: 39959996
Efficient R-group exploration in the vast chemical space, enabled by increasingly available building blocks or generative AI, remains an open challenge. Here, we developed an enhanced Free-Wilson QSAR model embedding...
2.
Kramer C, Chodera J, Damm-Ganamet K, Gilson M, Gunther J, Lessel U, et al.
J Chem Inf Model
. 2025 Feb;
65(5):2180-2190.
PMID: 39951479
Computational tools for structure-based drug design (SBDD) are widely used in drug discovery and can provide valuable insights to advance projects in an efficient and cost-effective manner. However, despite the...
3.
Nahal Y, Menke J, Martinelli J, Heinonen M, Kabeshov M, Janet J, et al.
J Cheminform
. 2024 Dec;
16(1):138.
PMID: 39654043
Machine learning (ML) systems have enabled the modelling of quantitative structure-property relationships (QSPR) and structure-activity relationships (QSAR) using existing experimental data to predict target properties for new molecules. These property...
4.
Olanders G, Testa G, Tibo A, Nittinger E, Tyrchan C
J Chem Inf Model
. 2024 Nov;
64(22):8481-8494.
PMID: 39484820
In the realm of biomedical research, understanding the intricate structure of proteins is crucial, as these structures determine how proteins function within our bodies and interact with potential drugs. Traditionally,...
5.
Tibo A, He J, Janet J, Nittinger E, Engkvist O
Nat Commun
. 2024 Aug;
15(1):7315.
PMID: 39183239
How many near-neighbors does a molecule have? This fundamental question in chemistry is crucial for molecular optimization problems under the similarity principle assumption. Generative models can sample molecules from a...
6.
He J, Tibo A, Janet J, Nittinger E, Tyrchan C, Czechtizky W, et al.
J Cheminform
. 2024 Aug;
16(1):95.
PMID: 39118113
Designing compounds with a range of desirable properties is a fundamental challenge in drug discovery. In pre-clinical early drug discovery, novel compounds are often designed based on an already existing...
7.
Zhang Y, Menke J, He J, Nittinger E, Tyrchan C, Koch O, et al.
J Cheminform
. 2023 Aug;
15(1):75.
PMID: 37649050
Siamese networks, representing a novel class of neural networks, consist of two identical subnetworks sharing weights but receiving different inputs. Here we present a similarity-based pairing method for generating compound...
8.
Hoyt C, Zdrazil B, Guha R, Jeliazkova N, Martinez-Mayorga K, Nittinger E
J Cheminform
. 2023 Jun;
15(1):62.
PMID: 37391855
No abstract available.
9.
Nittinger E, Clark A, Gaulton A, Zdrazil B
J Cheminform
. 2023 Apr;
15(1):46.
PMID: 37069670
No abstract available.
10.
Kwapien K, Nittinger E, He J, Margreitter C, Voronov A, Tyrchan C
ACS Omega
. 2022 Aug;
7(30):26573-26581.
PMID: 35936431
Matched molecular pairs (MMPs) are nowadays a commonly applied concept in drug design. They are used in many computational tools for structure-activity relationship analysis, biological activity prediction, or optimization of...